flink--会话模式与应用模式

flink-会话模式部署

会话情况：

添加依赖

<properties><flink.version>1.17.2</flink.version>
</properties>

<dependencies><dependency><groupId>org.apache.flink</groupId><artifactId>flink-streaming-java</artifactId><version>${flink.version}</version><scope>provided</scope></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-clients</artifactId><version>${flink.version}</version><scope>provided</scope></dependency>
<dependency><groupId>org.apache.flink</groupId><artifactId>flink-runtime-web</artifactId><version>${flink.version}</version><scope>provided</scope></dependency>
<dependency><groupId>org.apache.flink</groupId><artifactId>flink-connector-kafka</artifactId><version>${flink.version}</version></dependency>
<dependency><groupId>org.apache.flink</groupId><artifactId>flink-json</artifactId><version>1.13.6</version><scope>provided</scope></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-table-api-java-bridge</artifactId><version>${flink.version}</version><scope>provided</scope></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-table-planner-loader</artifactId><version>${flink.version}</version><scope>provided</scope></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-table-runtime</artifactId><version>${flink.version}</version><scope>provided</scope></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-connector-jdbc</artifactId><version>3.1.2-1.17</version></dependency><dependency><groupId>mysql</groupId><artifactId>mysql-connector-java</artifactId><version>8.0.29</version></dependency>

</dependencies>

<build><plugins><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-shade-plugin</artifactId><version>3.2.4</version><executions><execution><phase>package</phase><goals><goal>shade</goal></goals><configuration><artifactSet><excludes><exclude>com.google.code.findbugs:jsr305</exclude><exclude>org.slf4j:*</exclude><exclude>log4j:*</exclude><exclude>org.apache.hadoop:*</exclude></excludes></artifactSet><filters><filter><!-- Do not copy the signatures in the META-INF folder.Otherwise, this might cause SecurityExceptions when using the JAR. --><artifact>*:*</artifact><excludes><exclude>META-INF/*.SF</exclude><exclude>META-INF/*.DSA</exclude><exclude>META-INF/*.RSA</exclude></excludes></filter></filters><transformers combine.children="append"><transformerimplementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"></transformer></transformers></configuration></execution></executions></plugin></plugins>
</build>

java代码

package com.demo.day1;

import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.connector.kafka.source.KafkaSource;
import org.apache.flink.connector.kafka.source.enumerator.initializer.OffsetsInitializer;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;


import static org.apache.flink.connector.kafka.sink.KafkaSink.builder;

public class Demo1_WordCount {
public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();env.setParallelism(1);
KafkaSource<String> source = KafkaSource.<String>builder().setBootstrapServers("hadoop11:9092,hadoop12:9092,hadoop13:9092").setTopics("topic1").setValueOnlyDeserializer(new SimpleStringSchema()).setGroupId("g1").setStartingOffsets(OffsetsInitializer.latest()).build();
DataStream<String> ds = env.fromSource(source, WatermarkStrategy.noWatermarks(),"source");ds.flatMap(new FlatMapFunction<String, Tuple2<String,Integer>>() {@Overridepublic void flatMap(String value, Collector<Tuple2<String, Integer>> out) throws Exception {
String[] arr = value.split(",");for(String s1:arr){out.collect(Tuple2.of(s1,1));}}}).keyBy(new KeySelector<Tuple2<String, Integer>, String>() {@Overridepublic String getKey(Tuple2<String, Integer> value) throws Exception {return value.f0;}}).sum("f1").print();
env.execute();}

}

测试：

查看（数据同步）：

申请一个yarn会话：

打架包：

上传架包：

运行：

测试：

修改并行度为3进行测试：

打架包并上传：

查看：

生产者向topica（3个分区）发送数据：

向topica发送2000条数据

package com.kafka;

import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.producer.*;
import org.apache.kafka.common.serialization.StringSerializer;

import java.util.HashMap;
import java.util.Map;

public class producer{public static void main(String[] args) throws Exception{Map<String, Object> configs = new HashMap<>();configs.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG,"hadoop11:9092,hadoop12:9092,hadoop13:9092");configs.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class);configs.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
//设置自定义分区configs.put(ProducerConfig.PARTITIONER_CLASS_CONFIG,"com.kafka.MyPartitioner");

KafkaProducer<String,String> producer = new KafkaProducer<>(configs);for (int i=0;i<1000;i++){ProducerRecord producerRecord=new ProducerRecord("topica","kafka");producer.send(producerRecord, new Callback() {@Overridepublic void onCompletion(RecordMetadata recordMetadata, Exception e) {if (e == null){System.out.println("发送成功："+recordMetadata.partition());System.out.println("发送成功："+recordMetadata.topic());System.out.println("发送成功："+recordMetadata.offset());}}});}
producer.close();}
}

发送分区随机：

查看数据：

向topica发送1000000条数据：

package com.kafka;

import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.producer.*;
import org.apache.kafka.common.serialization.StringSerializer;

import java.util.HashMap;
import java.util.Map;

public class producer{public static void main(String[] args) throws Exception{Map<String, Object> configs = new HashMap<>();configs.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG,"hadoop11:9092,hadoop12:9092,hadoop13:9092");configs.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class);configs.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
//设置自定义分区configs.put(ProducerConfig.PARTITIONER_CLASS_CONFIG,"com.kafka.MyPartitioner");

KafkaProducer<String,String> producer = new KafkaProducer<>(configs);for (int i=0;i<1000000;i++){ProducerRecord producerRecord=new ProducerRecord("topica",i%3,null,"kafka");producer.send(producerRecord, new Callback() {@Overridepublic void onCompletion(RecordMetadata recordMetadata, Exception e) {if (e == null){System.out.println("发送成功："+recordMetadata.partition());System.out.println("发送成功："+recordMetadata.topic());System.out.println("发送成功："+recordMetadata.offset());}}});}
producer.close();}
}

查看数据：

flink-应用模式部署

1. 上传flink的lib和plugins到HDFS上
hdfs dfs -mkdir /flink-dist
hdfs dfs -put /opt/installs/flink/lib   /flink-dist
hdfs dfs -put /opt/installs/flink/plugins/ /flink-dist
2. 上传自己的jar包到HDFS
hdfs dfs -mkdir /my-flinkjars
hdfs dfs -put /opt/flinkjob/flink-test-1.0-SNAPSHOT.jar /my-flinkjars
3. 提交作业
flink run-application \
-t yarn-application   \
-Dyarn.provided.lib.dirs="hdfs://hdfs-cluster/flink-dist"     \
-c com.demo.day1.Demo1_WordCount  \
hdfs://hdfs-cluster/my-flinkjars/flink-test-1.0-SNAPSHOT.jar

提交作业后查看yarn：